class: center, middle, inverse, title-slide .title[ # Epidemiology ] .author[ ###
Claire Prince
• 04-Nov-2022 ] .institute[ ### Zifo RnD Solutions ] --- exclude: true count: false <!-- ------------ Only edit title, subtitle & author above this ------------ --> --- ## What is epidemiology <br> “The study of the distribution and determinants of disease in health-related states and events in defined populations, and the application of this study to the control of health problems” <br><br> Last J. Dictionary of epidemiology, OUP, 2001 <br><br><br><br> <img src="data:image/png;base64,#data/epidemiology/whatisepi.png" width="70%" style="display: block; margin: auto;" /> --- ## Epidemiological studies <br> <img src="data:image/png;base64,#data/epidemiology/epistudies.png" width="70%" style="display: block; margin: auto;" /> --- ## Evaluating rates of disease * Incidence <br> + The rate at which **new** cases occur in a population during a specified period. <br> * Prevalence <br> + The proportion of **existing** cases in a population at one point in time. <br> * Attributable proportion <br> + The proportion of a disease in a group that is exposed to a **particular factor** which can be attributed to their exposure to that factor. <br> * Risk ratio <br> + Ratio of the probability of an event occurring in the **exposed** population to the probability of it occurring in the **unexposed** population <br> * Incidence rate ratio <br> + Ratio of the incidence rate of disease in the **exposed** population compared to that in the **unexposed** population <br> * Odds ratio <br> + Odds of an event is the probability that an individual **experiences the event** divided by the probability that **they do not**. --- ## Calculating odds and risk ratios <br> <img src="data:image/png;base64,#data/epidemiology/calratios.png" width="70%" style="display: block; margin: auto;" /> --- ## Diagnostic tests * The **sensitivity** of a test is the proportion of people who test **positive** among all those who **actually have the disease**. <br><br> * The **specificity** of a test is the proportion of people who test **negative** among all those who **actually do not have that disease**. <br><br> * The **positive predictive value** is the **probability** that following a **positive** test result, that individual will **truly have that disease**. <br><br> * The **negative predictive value** is the **probability** that following a **negative** test result, that individual will **truly not have that disease**. <br><br> <img src="data:image/png;base64,#data/epidemiology/diagtests.png" width="60%" style="display: block; margin: auto;" /> --- ## Measurement error and bias * Selection bias <br><br> * Information bias <br> + Recall bias <br><br> + Observer bias <br><br> * Consequences of information bias <br> + Non-differential misclassifcation <br><br> + Differential misclassification <br> --- ## Confounding/mediation <br><br><br><br> <img src="data:image/png;base64,#data/epidemiology/confoud-mediate.png" width="80%" style="display: block; margin: auto;" /> <br><br><br><br> --- ## Confounding <br> * A **confounder** is a variable that influences both the exposure and outcome, causing a spurious association <br><br><br><br><br><br> <img src="data:image/png;base64,#data/epidemiology/confound.png" width="80%" style="display: block; margin: auto;" /> <br> --- ## Mediation <br> * A **mediator** is a variable that is influenced by the exposure and in turn influences the outcome. <br><br><br><br><br><br><br> <img src="data:image/png;base64,#data/epidemiology/mediate.png" width="80%" style="display: block; margin: auto;" /> <br> --- ## Genetic epidemiology <br><br> * Understanding how genetic factors influence traits and disease. <br><br><br> * Twin studies <br><br> * Linkage analysis <br><br> * Association <br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br> --- ## Twin studies * Comparison of monozygotic and dizygotic twins <br><br><br><br><br><br> <img src="data:image/png;base64,#data/epidemiology/twins.png" width="100%" style="display: block; margin: auto;" /> <br> --- ## Linkage analysis * Within family analysis used to identify **rare variants** that influence rare disease. <br><br><br><br><br> <img src="data:image/png;base64,#data/epidemiology/linkage.png" width="90%" style="display: block; margin: auto;" /> <br> --- ## Genome wide association studies <br> * Test associations between **genetic variants** and a specific trait and disease <br><br> * In GWAS' the most tested genetic variants are **single-nucleotide polymorphisms** (SNPs) <br><br><br> <img src="data:image/png;base64,#data/epidemiology/GWAS.png" width="50%" style="display: block; margin: auto;" /> <br> --- ## GWAS quality control <br><br><br><br><br> <img src="data:image/png;base64,#data/epidemiology/gwasqc.png" width="100%" style="display: block; margin: auto;" /> <br> --- ## GWAS steps <br> <img src="data:image/png;base64,#data/epidemiology/gwassteps.png" width="70%" style="display: block; margin: auto;" /> <br> --- ## Current GWAS limitations <br> * Most published GWAS’ are performed in individuals of European descent <br><br><br> * Variants identified for a specific complex trait or disease only account for a **small proportion** of the estimated heritability <br><br><br> * Doesn’t necessarily identify **causal** variants and genes <br><br><br> * Limited clinical predictive value --- ## Beyond GWAS <br> * Statistical fine-mapping <br><br><br> * SNP enrichment <br><br><br> * Quantitative trait loci mapping <br><br><br> * Colocalization <br><br><br> * Functional studies <br><br><br> * Clinical significance <img src="data:image/png;base64,#data/epidemiology/beyondgwas.png" width="60%" style= "float:right;position: relative; top: -420px;" style="display: block; margin: auto;" /> --- ## Other 'omics <br><br><br><br> <img src="data:image/png;base64,#data/epidemiology/omics.png" width="70%" style="display: block; margin: auto;" /> <br> --- ## Epigenome <br><br> <img src="data:image/png;base64,#data/epidemiology/epigenome.png" width="80%" style="display: block; margin: auto;" /> <br> --- ## Transcriptome wide association studies * A gene-based association approach that investigates associations between **genetically regulated gene expression** and complex diseases or traits. <br><br><br> <img src="data:image/png;base64,#data/epidemiology/twas.png" width="90%" style="display: block; margin: auto;" /> <br> <br> --- ## Mendelian randomization * A method that uses genetic variants as instrumental variables to explore causal effects of risk factors on outcomes in observational epidemiological studies. <br><br><br> <img src="data:image/png;base64,#data/epidemiology/MR1.png" width="90%" style="display: block; margin: auto;" /> <br> --- ## Mendelian randomization <br><br><br> <img src="data:image/png;base64,#data/epidemiology/MR2.png" width="100%" style="display: block; margin: auto;" /> <br> --- ## Summary * Epidemiology is “The study of the distribution and determinants of disease in health-related states and events in defined populations, and the application of this study to the control of health problems” <br><br> * Genetic epidemiology allow us to understand the genetic risk factors for health traits and disease <br><br> * There are methods that utilise GWAS findings to prioritize variants for further study which may lead to clinical significance <br><br> * Other 'omics such as the epigenome and transcriptome can be studies to aid in the understanding of disease in populations <br><br> * Mendelian randomization uses GWAS with the aim to isolate causal effects by avoiding confoundings and reverse causation. --- ## Further reading * Epidemiology for the uninitiated. From: https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated * Paternoster L, Tilling K, Davey Smith G. Genetic epidemiology and Mendelian randomization for informing disease therapeutics: Conceptual and methodological challenges. PLoS Genet. 2017 Oct 5;13(10):e1006944. * Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet. 2019 Aug;20(8):467-484. * Peterson RE, Kuchenbaecker K, Walters RK, Chen CY, Popejoy AB, Periyasamy S, et al. Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations. Cell. 2019 Oct 17;179(3):589-603. * Cano-Gamez E, Trynka G. From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases. Front Genet. 2020 May 13;11:424. * Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018 Aug;19(8):491-504. * Lichou F, Trynka G. Functional studies of GWAS variants are gaining momentum. Nat Commun. 2020 Dec 8;11(1):6283. * Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011 Jul 12;12(8):529-41. * Li B, Ritchie MD. From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries. Front Genet. 2021 Sep 30;12:713230. * Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014 Sep 15;23(R1):R89-98. <!-- --------------------- Do not edit this and below --------------------- --> --- name: end_slide class: end-slide, middle count: false # Thank you. Questions? .end-text[ <p class="smaller"> <span class="small" style="line-height: 1.2;">Graphics from </span><img src="./assets/freepik.jpg" style="max-height:20px; vertical-align:middle;"><br> Created: 04-Nov-2022 • James Ashmore • <a href="https://www.zifornd.com/category/omics-bioinformatics">Bioinformatics</a> • <a href="https://www.zifornd.com">Zifo</a> </p> ]